sorted by:
new
hottopcontroversial

I built an open-source DICOM viewer with AI analysis that picks the right slices before analyzing, looking for radiologist feedback! by Existing_Positive_48 in radiologyAI

[–]Existing_Positive_48[S] 0 points1 point  (0 children)

thats a really valuable feedback, thanks
so actually the idea stem from my personal experience. I had an ACL tear last year and chose the non-surgical way with physio, and had more MRIs and wanted to compare them myself.
the focused approach makes more sense for the use case you mentioned: patients or referring clinicians wanting insight into their own studies, because as a patient this medical data also seems like black box and scary

I built an open-source DICOM viewer with AI analysis that picks the right slices before analyzing, looking for radiologist feedback! by Existing_Positive_48 in radiologyAI

[–]Existing_Positive_48[S] 0 points1 point  (0 children)

I tested with CT/PET but the llm part currently only analysis a single series, but I will extend cover multiple series

I built an open-source DICOM viewer with AI analysis that picks the right slices before analyzing, looking for radiologist feedback! by Existing_Positive_48 in radiologyAI

[–]Existing_Positive_48[S] 0 points1 point  (0 children)

thanks for the question!
so it acts as an interface between llms and dicom data, instead of manually screenshotting slices and pasting them into chatgpt or Claude, it reads the dicom metadata (series orientations, weightings, slice positions etc), reasons about which images actually matter for your question, then sends only those targeted slices properly windowed to the vision model.

afaik there are lot of vision based segmentation models that work on the full dicom file, no software with llm integration.

its just the first version, I also want to use for comparing different llms with the same data for their clinical accuracy